To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure firstname.lastname@example.org
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
HIV-1 molecular surveillance provides a new approach to explore transmission risks and targeted interventions. From January to June 2021, 663 newly reported HIV-1 cases were recruited in Zhaotong City, Yunnan Province, China. The distribution characteristics of HIV-1 subtypes and HIV-1 molecular network were analysed. Of 542 successfully subtyped samples, 12 HIV-1 strains were identified. The main strains were CRF08_BC (47.0%, 255/542), CRF01_AE (17.0%, 92/542), CRF07_BC (17.0%, 92/542), URFs (8.7%, 47/542), and CRF85_BC (6.5%, 35/542). CRF08_BC was commonly detected among Zhaotong natives, illiterates, and non-farmers and was mostly detected in Zhaoyang County. CRF01_AE was frequently detected among married and homosexual individuals and mostly detected in Weixin and Zhenxiong counties. Among the 516 pol sequences, 187 (36.2%) were clustered. Zhaotong natives, individuals aged ≥60 years, and illiterate individuals were more likely to be found in the network. Assortativity analysis showed that individuals were more likely to be genetically associated when stratified by age, education level, occupation, and reporting area. The genetic diversity of HIV-1 reflects the complexity of local HIV epidemics. Molecular network analyses revealed the subpopulations to focus on and the characteristics of the risk networks. The results will help optimise local prevention and control strategies.
Jogayle Howard, National Zoological Park,
Yan Huang, China Conservation and Research Center for the Giant Panda,
Pengyan Wang, China Research and Conservation Center for the Giant Panda,
Desheng Li, China Conservation and Research Center for the Giant Panda,
Guiquan Zhang, China Research and Conservation Center for the Giant Panda,
Rong Hou, Chengdu Research Base of Giant Panda Breeding,
Zhihe Zhang, Chengdu Research Base of Giant Panda Breeding,
Barbara S. Durrant, Conservation and Research for Endangered Species,
Rebecca Spindler, Toronto Zoo,
Hemin Zhang, China Conservation and Research Center for the Giant Panda,
Anju Zhang, Chengdu Giant Panda Breeding Research Foundation,
David E. Wildt, National Zoological Park
Historically, the breeding of giant pandas in ex situ programmes has been difficult due to behavioural incompatibility and interanimal aggression. Because some individuals fail to mate naturally, the potential loss of valuable genes is a major concern to effective genetic management (see Chapter 21). Consistently successful artificial insemination (AI) would allow incorporating genetically valuable males with behavioural or physical anomalies into the gene pool. This strategy becomes even more powerful when used in the context of a genome resource bank (GRB), an organised repository of cryopreserved biomaterials (tissue, blood, DNA and sperm) (see Chapter 7). The use of sperm cryopreservation and AI allows the movement of genes among zoos and breeding centres without needing to transfer animals, which is both stressful and costly.
‘Assisted breeding’ refers to the tools and techniques associated with helping a pair of animals propagate, from AI to embryo transfer to cloning, among others (Howard, 1999; Pukazhenthi & Wildt, 2004). With the exception of AI, there is not much need for most other assisted-breeding techniques for the giant panda. As will be demonstrated here, AI is quite adequate for dealing with most cases of infertility or with helping to maintain adequate gene diversity in the captive population. In fact, the major breeding facilities, especially the China Conservation and Research Centre for the Giant Panda (hereafter referred to as the Wolong Breeding Centre) and the Chengdu Research Base of Giant Panda Breeding, routinely use AI to increase pregnancy success.
Increasing breeding success in the giant panda requires a better understanding of its complex reproductive biology. We know that the female is typically mono-oestrus during a breeding season which occurs from February to May (within and outside China). Behavioural and physiological changes associated with pro-oestrus and oestrus last one to two weeks, during which the female exhibits proceptive behaviours, such as scent marking, to advertise her sexual receptivity (Lindburg et al., 2001). During the peri-ovulatory interval, receptive behaviours (e.g. tail-up lordotic posture) climax with copulation generally occurring over a one- to three-day interval. Birthing occurs from June to October with a gestation of 85 to 185 days (Zhu et al., 2001). This unusually wide gestation span is due to the phenomenon of delayed implantation, a varied interval before the conceptus implants in the uterus and begins foetal development. The driving force behind implantation in this species is unknown. The giant panda also experiences pseudopregnancy, whereby the female exhibits behavioural, physiological and hormonal changes similar to pregnancy.
Behavioural and physiological cues associated with both pregnancy and pseudopregnancy include decreased appetite, nest-building and cradling behaviours, vulvar swelling and colouration, mammary gland enlargement and lethargy. Additionally, temporal and quantitative progesterone patterns (tracked by assessing urinary hormone by-products and progestins) are indistinguishable between pregnancy and pseudopregnancy. Therefore, no definitive test currently exists for identifying pregnant from pseudopregnant giant pandas.
Email your librarian or administrator to recommend adding this to your organisation's collection.